import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_predict, train_test_split
from sklearn import datasets
from sklearn import metrics
import numpy as np

# 读取数据
names = ['成色', '市场价格', '颜色', '材质', '是否限量', '回收价格']
feature_names = ['成色', '市场价格']
target_name = ['回收价格']
df = pd.read_csv('data.csv', names=names, sep='\t', header=0)

# (df['回收价格'] < 1000)or(df['回收价格'] > df['市场价格'])or(df['回收价格'] > 10000)
drop_index1 = df[((df['回收价格'] < 1000))|(df['回收价格'] > 10000)|(df['回收价格'] > df['市场价格'])].index.values

print(drop_index1)